On the use of weighting in LCA: translating decision makers’ preferences into weights via linear programming

Purpose

The main goal of any life cycle assessment (LCA) study is to identify solutions leading to environmental savings. In conventional LCA studies, practitioners select from some alternatives the one which better matches their preferences. This task is sometimes simplified by ranking these alternatives using an aggregated indicator defined by attaching weights to impacts. We address here the inverse problem. That is, given an alternative, we aim to determine the weights for which that solution becomes optimal.

Methods

We propose a method based on linear programming (LP) that determines, for a given alternative, the ranges within which the weights attached to a set of impact metrics must lie so that when a weighting combination of these impacts is optimized, the alternative can be optimal, while if the weights fall outside this range, it is guaranteed that the solution will be suboptimal. A large weight value implies that the corresponding LCA impact is given more importance, while a low value implies the converse. Furthermore, we provide a rigorous mathematical analysis on the implications of using weighting schemes in LCA, showing that this practice guides decision-making towards the adoption of some specific alternatives (those lying on the convex envelope of the resulting trade-off curve).

Results and discussion

A case study based on the design of hydrogen infrastructures is taken as a test bed to illustrate the capabilities of the approach presented. Given are a set of production and storage technologies available to produce and deliver hydrogen, a final demand, and cost and environmental data. A set of designs, each achieving a unique combination of cost and LCA impact, is considered. For each of them, we calculate the minimum and maximum weight to be given to every LCA impact so that the alternative can be optimal among all the candidate designs. Numerical results show that solutions with lower impact are selected when decision makers are willing to pay larger monetary penalties for the environmental damage caused.

Conclusions

LP can be used in LCA to translate the decision makers’ preferences into weights. This information is rather valuable, particularly when these weights represent economic penalties, as it allows screening and ranking alternatives on the basis of a common economic basis. Our framework is aimed at facilitating decision making in LCA studies and defines a general framework for comparing alternatives that show different performance in a wide variety of impact metrics.     

Dissecting the energy metabolism in Mycoplasma pneumoniae through genome‐scale metabolic modeling

Mycoplasma pneumoniae, a threatening pathogen with a minimal genome, is a model organism for bacterial systems biology for which substantial experimental information is available. With the goal of understanding the complex interactions underlying its metabolism, we analyzed and characterized the metabolic network of M. pneumoniae in great detail, integrating data from different omics analyses under a range of conditions into a constraint‐based model backbone. Iterating model predictions, hypothesis generation, experimental testing, and model refinement, we accurately curated the network and quantitatively explored the energy metabolism. In contrast to other bacteria, M. pneumoniae uses most of its energy for maintenance tasks instead of growth. We show that in highly linear networks the prediction of flux distributions for different growth times allows analysis of time‐dependent changes, albeit using a static model. By performing an in silico knock‐out study as well as analyzing flux distributions in single and double mutant phenotypes, we demonstrated that the model accurately represents the metabolism of M. pneumoniae. The experimentally validated model provides a solid basis for understanding its metabolic regulatory mechanisms.     

Metaproteogenomic insights beyond bacterial response to naphthalene exposure and bio-stimulation

Microbial metabolism in aromatic-contaminated environments has important ecological implications, and obtaining a complete understanding of this process remains a relevant goal. To understand the roles of biodiversity and aromatic-mediated genetic and metabolic rearrangements, we conducted ‘OMIC'' investigations in an anthropogenically influenced and polyaromatic hydrocarbon (PAH)-contaminated soil with (Nbs) or without (N) bio-stimulation with calcium ammonia nitrate, NH₄NO₃ and KH₂PO₄ and the commercial surfactant Iveysol, plus two naphthalene-enriched communities derived from both soils (CN2 and CN1, respectively). Using a metagenomic approach, a total of 52, 53, 14 and 12 distinct species (according to operational phylogenetic units (OPU) in our work equivalent to taxonomic species) were identified in the N, Nbs, CN1 and CN2 communities, respectively. Approximately 10 out of 95 distinct species and 238 out of 3293 clusters of orthologous groups (COGs) protein families identified were clearly stimulated under the assayed conditions, whereas only two species and 1465 COGs conformed to the common set in all of the mesocosms. Results indicated distinct biodegradation capabilities for the utilisation of potential growth-supporting aromatics, which results in bio-stimulated communities being extremely fit to naphthalene utilisation and non-stimulated communities exhibiting a greater metabolic window than previously predicted. On the basis of comparing protein expression profiles and metagenome data sets, inter-alia interactions among members were hypothesised. The utilisation of curated databases is discussed and used for first time to reconstruct ‘presumptive'' degradation networks for complex microbial communities.     

Design‐to‐Device Approach Affords Panchromatic Co‐Sensitized Solar Cells

Data‐driven materials discovery has become increasingly important in identifying materials that exhibit specific, desirable properties from a vast chemical search space. Synergic prediction and experimental validation are needed to accelerate scientific advances related to critical societal applications. A design‐to‐device study that uses high‐throughput screens with algorithmic encodings of structure–property relationships is reported to identify new materials with panchromatic optical absorption, whose photovoltaic device applications are then experimentally verified. The data‐mining methods source 9431 dye candidates, which are auto‐generated from the literature using a custom text‐mining tool. These candidates are sifted via a data‐mining workflow that is tailored to identify optimal combinations of organic dyes that have complementary optical absorption properties such that they can harvest all available sunlight when acting as co‐sensitizers for dye‐sensitized solar cells (DSSCs). Six promising dye combinations are shortlisted for device testing, whereupon one dye combination yields co‐sensitized DSSCs with power conversion efficiencies comparable to those of the high‐performance, organometallic dye, N719. These results demonstrate how data‐driven molecular engineering can accelerate materials discovery for panchromatic photovoltaic or other applications.     

Effect of interannual precipitation variability on dryland productivity: A global synthesis

Climate‐change assessments project increasing precipitation variability through increased frequency of extreme events. However, the effects of interannual precipitation variance per se on ecosystem functioning have been largely understudied. Here, we report on the effects of interannual precipitation variability on the primary production of global drylands, which include deserts, steppes, shrublands, grasslands, and prairies and cover about 40% of the terrestrial earth surface. We used a global database that has 43 datasets, which are uniformly distributed in parameter space and each has at least 10 years of data. We found (a) that at the global scale, precipitation variability has a negative effect on aboveground net primary production. (b) Expected increases in interannual precipitation variability for the year 2,100 may result in a decrease of up to 12% of the global terrestrial carbon sink. (c) The effect of precipitation interannual variability on dryland productivity changes from positive to negative along a precipitation gradient. Arid sites with mean precipitation under 300 mm/year responded positively to increases in precipitation variability, whereas sites with mean precipitation over 300 mm/year responded negatively. We propose three complementary mechanisms to explain this result: (a) concave‐up and concave‐down precipitation–production relationships in arid vs. humid systems, (b) shift in the distribution of water in the soil profile, and (c) altered frequency of positive and negative legacies. Our results demonstrated that enhanced precipitation variability will have direct impacts on global drylands that can potentially affect the future terrestrial carbon sink.     

The PUA domain - a structural and functional overview

The pseudouridine synthase and archaeosine transglycosylase (PUA) domain is a compact and highly conserved RNA-binding motif that is widespread among diverse types of proteins from the three kingdoms of life. Its three-dimensional architecture is well established, and the structures of several PUA-RNA complexes reveal a common RNA recognition surface, but also some versatility in the way in which the motif binds to RNA. The PUA domain is often part of RNA modification enzymes and ribonucleoproteins, but it has also been unexpectedly found fused to enzymes involved in proline biosynthesis, where it plays an unknown role. The functional impact of the domain varies with the protein studied, ranging from minor to essential effects. PUA motifs are involved in dyskeratosis congenita and cancer, pointing to links between RNA metabolism and human diseases.     

The low-resolution structural models of hepatitis C virus RNA subdomain 5BSL3.2 and its distal complex with domain 3′X point to conserved regulatory mechanisms within the Flaviviridae family

Subdomain 5BSL3.2 of hepatitis C virus RNA lies at the core of a network of distal RNA–RNA contacts that connect the 5′ and 3′ regions of the viral genome and regulate the translation and replication stages of the viral cycle. Using small-angle X-ray scattering and NMR spectroscopy experiments, we have determined at low resolution the structural models of this subdomain and its distal complex with domain 3′X, located at the 3′-terminus of the viral RNA chain. 5BSL3.2 adopts a characteristic ‘L’ shape in solution, whereas the 5BSL3.2–3′X distal complex forms a highly unusual ‘Y’-shaped kissing junction that blocks the dimer linkage sequence of domain 3′X and promotes translation. The structure of this complex may impede an effective association of the viral polymerase with 5BSL3.2 and 3′X to start negative-strand RNA synthesis, contributing to explain the likely mechanism used by these sequences to regulate viral replication and translation. In addition, sequence and shape features of 5BSL3.2 are present in functional RNA motifs of flaviviruses, suggesting conserved regulatory processes within the Flaviviridae family.